{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast\n",
    "from tqdm import tqdm\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gc\n",
    "import pickle\n",
    "import json\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"Pickles/lemmatized.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Models\n",
    "\n",
    "1. Preprocess dataframe\n",
    "2. Load Word Embeddings\n",
    "3. Train Models\n",
    "4. Model Evaluation\n",
    "5. Continue Training for More Epochs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.layers import Dense, Dropout, GRU,CuDNNGRU,Input, LSTM, Embedding, Bidirectional\n",
    "from keras.layers import Flatten, Conv1D, MaxPooling1D, GlobalMaxPooling1D, TimeDistributed, BatchNormalization\n",
    "from keras.layers import concatenate as lconcat\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras import backend as K\n",
    "\n",
    "\n",
    "#sess_config.gpu_options.allow_growth = True\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from keras.utils import np_utils,plot_model, multi_gpu_model\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 3888345772746440188\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 11273460122\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "    link {\n",
      "      device_id: 1\n",
      "      type: \"StreamExecutor\"\n",
      "      strength: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "incarnation: 15903324187681983473\n",
      "physical_device_desc: \"device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7\"\n",
      ", name: \"/device:GPU:1\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 11287530701\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "    link {\n",
      "      type: \"StreamExecutor\"\n",
      "      strength: 1\n",
      "    }\n",
      "  }\n",
      "}\n",
      "incarnation: 6246870232400548417\n",
      "physical_device_desc: \"device: 1, name: Tesla K80, pci bus id: 0000:00:05.0, compute capability: 3.7\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Define number of words, and embedding dimensions\n",
    "max_words = 34603\n",
    "embed_dim = 100\n",
    "\n",
    "def load_embeddings(vec_file):\n",
    "    print(\"Loading Glove Model\")\n",
    "    f = open(vec_file,'r')\n",
    "    model = {}\n",
    "    for line in f:\n",
    "        splitLine = line.split()\n",
    "        word = splitLine[0]\n",
    "        embedding = np.array([float(val) for val in splitLine[1:]])\n",
    "        model[word] = embedding\n",
    "    print(\"Done. {} words loaded!\".format(len(model)))\n",
    "    return model\n",
    "\n",
    "def tokenize_and_pad(docs,max_words=max_words):\n",
    "    global t\n",
    "    t = Tokenizer()\n",
    "    t.fit_on_texts(docs)\n",
    "    docs = pad_sequences(sequences = t.texts_to_sequences(docs),maxlen = max_words, padding = 'post')\n",
    "    global vocab_size\n",
    "    vocab_size = len(t.word_index) + 1\n",
    "    \n",
    "    return docs\n",
    "\n",
    "def oversample(X,docs,y):\n",
    "    # Get number of rows with imbalanced class\n",
    "    target = y.sum().idxmax()\n",
    "    n = y[target].sum()\n",
    "    # identify imbalanced targets\n",
    "    imbalanced = y.drop(target,axis=1)\n",
    "    #For each target, create a dataframe of randomly sampled rows, append to list\n",
    "    append_list =  [y.loc[y[col]==1].sample(n=n-y[col].sum(),replace=True,random_state=20) for col in imbalanced.columns]\n",
    "    append_list.append(y)\n",
    "    y = pd.concat(append_list,axis=0)\n",
    "    # match y indexes on other inputs\n",
    "    X = X.loc[y.index]\n",
    "    docs = pd.DataFrame(docs_train,index=y_train.index).loc[y.index]\n",
    "    assert (y.index.all() == X.index.all() == docs.index.all())\n",
    "    return X,docs.values,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlb = MultiLabelBinarizer()\n",
    "df = df.join(pd.DataFrame(mlb.fit_transform(df.pop('items')),columns=mlb.classes_,),sort=False,how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Separate into X and Y\n",
    "cols = ['GICS Sector','vix','rm_week','rm_month', 'rm_qtr', 'rm_year']\n",
    "cols.extend(list(mlb.classes_))\n",
    "X = df[cols]\n",
    "docs = df['processed_text']\n",
    "\n",
    "y = df['signal']\n",
    "\n",
    "# Get Dummies\n",
    "\n",
    "docs = tokenize_and_pad(docs)\n",
    "X = pd.get_dummies(columns = ['GICS Sector'],prefix=\"sector\",data=X)\n",
    "y = pd.get_dummies(columns=['signal'],data=y)\n",
    "\n",
    "aux_shape = len(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into train and test data\n",
    "X_train, X_test, y_train, y_test, docs_train, docs_test = train_test_split(X, y,docs,\n",
    "                                                    stratify=y, \n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/Yusuf/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/home/Yusuf/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/home/Yusuf/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "cont_features = ['vix','rm_week','rm_month', 'rm_qtr', 'rm_year']\n",
    "aux_features = cont_features + [item for item in mlb.classes_]\n",
    "x_scaler = StandardScaler()\n",
    "X_train[cont_features] = x_scaler.fit_transform(X_train[cont_features])\n",
    "X_test[cont_features] = x_scaler.transform(X_test[cont_features])\n",
    "\n",
    "X_train, docs_train, y_train = oversample(X_train, docs_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Glove Model\n",
      "Done. 400000 words loaded!\n"
     ]
    }
   ],
   "source": [
    "embeddings_index = load_embeddings(\"glove.6B.100d.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of null word embeddings: 6391114\n"
     ]
    }
   ],
   "source": [
    "words_not_found = []\n",
    "\n",
    "embedding_matrix = np.zeros((vocab_size, embed_dim))\n",
    "for word, i in t.word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix[i] = embedding_vector\n",
    "    else:\n",
    "        words_not_found.append(word)\n",
    "print('number of null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# define roc_callback, inspired by https://github.com/keras-team/keras/issues/6050#issuecomment-329996505\n",
    "def auc_roc(y_true, y_pred):\n",
    "    # any tensorflow metric\n",
    "    value, update_op = tf.metrics.auc(y_true,y_pred)\n",
    "\n",
    "    # find all variables created for this metric\n",
    "    metric_vars = [i for i in tf.local_variables() if 'auc_roc' in i.name.split('/')[1]]\n",
    "\n",
    "    # Add metric variables to GLOBAL_VARIABLES collection.\n",
    "    # They will be initialized for new session.\n",
    "    for v in metric_vars:\n",
    "        tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)\n",
    "\n",
    "    # force to update metric values\n",
    "    with tf.control_dependencies([update_op]):\n",
    "        value = tf.identity(value)\n",
    "        return value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Build & Train Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'aux_shape' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-fd61ffb620ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_classes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marchitecture\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maux_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maux_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0membedding_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membedding_matrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_seq_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/cpu:0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mmain_input\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_seq_len\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'doc_input'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         main = Embedding(input_dim = vocab_size,\n",
      "\u001b[0;31mNameError\u001b[0m: name 'aux_shape' is not defined"
     ]
    }
   ],
   "source": [
    "def build_model(output_classes,architecture,aux_shape=aux_shape,vocab_size=vocab_size,embed_dim=embed_dim,embedding_matrix=embedding_matrix,max_seq_len=max_words):\n",
    "    \n",
    "    with tf.device('/cpu:0'):\n",
    "        main_input= Input(shape=(max_seq_len,),name='doc_input')\n",
    "        main = Embedding(input_dim = vocab_size,\n",
    "                            output_dim = embed_dim,\n",
    "                            weights=[embedding_matrix], \n",
    "                            input_length=max_seq_len, \n",
    "                            trainable=False)(main_input)\n",
    "\n",
    "    if architecture == 'mlp':\n",
    "        # Densely Connected Neural Network (Multi-Layer Perceptron)\n",
    "        main = Dense(32, activation='relu')(main)\n",
    "        main = Dropout(0.2)(main)\n",
    "        main = Flatten()(main)\n",
    "    elif architecture == 'cnn':\n",
    "        # 1-D Convolutional Neural Network\n",
    "        main = Conv1D(64, 3, strides=1, padding='same', activation='relu')(main)\n",
    "        #Cuts the size of the output in half, maxing over every 2 inputs\n",
    "        main = MaxPooling1D(pool_size=3)(main)\n",
    "        main = Dropout(0.2)(main)\n",
    "        main = Conv1D(32, 3, strides=1, padding='same', activation='relu')(main)\n",
    "        main = GlobalMaxPooling1D()(main)\n",
    "        #model.add(Dense(output_classes, activation='softmax'))\n",
    "    elif architecture == 'rnn':\n",
    "        # LSTM network\n",
    "        main = Bidirectional(CuDNNGRU(64, return_sequences=False),merge_mode='concat')(main)\n",
    "        main = BatchNormalization()(main)\n",
    "    elif architecture ==\"rnn_cnn\":\n",
    "        main = Conv1D(64, 5, padding='same', activation='relu')(main)\n",
    "        main = MaxPooling1D()(main)\n",
    "        main = Dropout(0.2)(main)\n",
    "        main = Bidirectional(CuDNNGRU(32,return_sequences=False),merge_mode='concat')(main)\n",
    "        main = BatchNormalization()(main)\n",
    "   \n",
    "    else:\n",
    "        print('Error: Model type not found.')\n",
    "        \n",
    "    auxiliary_input = Input(shape=(aux_shape,), name='aux_input')\n",
    "    x = lconcat([main, auxiliary_input])\n",
    "    x = Dense(32, activation='relu')(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    x = Dense(32, activation='relu')(x)\n",
    "    x = Dense(32, activation='relu')(x)\n",
    "    main_output = Dense(output_classes, activation='sigmoid', name='main_output')(x)\n",
    "    model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output],name=architecture)\n",
    "        \n",
    "    #sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    model = multi_gpu_model(model)\n",
    "    model.compile('adam', 'categorical_crossentropy',metrics=['accuracy',auc_roc])\n",
    "    \n",
    "    return model\n",
    "\n",
    "def plot_metrics(model_dict,metric,x_label,y_label):\n",
    "    plots = 1\n",
    "    plt.figure(figsize=[15,10])\n",
    "    for model, history in model_dict.items():\n",
    "        plt.subplot(2,2,plots)\n",
    "        plt.plot(history[metric])\n",
    "        #plt.plot(history.history['val_acc'])\n",
    "        plt.title('{0} {1}'.format(model,metric))\n",
    "        plt.ylabel(y_label)\n",
    "        plt.xlabel(x_label)\n",
    "        plots += 1\n",
    "    #plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"Graphs/{}.png\".format(metric),format=\"png\")\n",
    "    plt.show()\n",
    "    \n",
    "def gen():\n",
    "    print('generator initiated')\n",
    "    idx = 0\n",
    "    while True:\n",
    "        yield [docs_train[:32], X_train[:32]], y_train[:32]\n",
    "        print('generator yielded a batch %d' % idx)\n",
    "        idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save data\n",
    "np.save(\"Pickles/docs_train.npy\",docs_train)\n",
    "np.save(\"Pickles/docs_test.npy\",docs_test)\n",
    "\n",
    "X_train.to_pickle(\"Pickles/X_train.pkl\")\n",
    "X_test.to_pickle(\"Pickles/X_test.pkl\")\n",
    "\n",
    "y_train.to_pickle(\"Pickles/y_train.pkl\")\n",
    "y_test.to_pickle(\"Pickles/y_test.pkl\") \n",
    "\n",
    "np.save(\"Pickles/embedding_matrix.npy\",embedding_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mlp = build_model(3,\"mlp\")\n",
    "\n",
    "model_dict[\"mlp\"] = mlp.fit([docs_train,X_train],y_train,batch_size=64,epochs=10,verbose=1) \n",
    "\n",
    "mlp.save(\"Data/models/mlp.hdf5\")\n",
    "with open('Data/trainHistory/mlp.pkl', 'wb') as file_pi:\n",
    "    pickle.dump(model_dict[\"mlp\"], file_pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "20457/20457 [==============================] - 3727s 182ms/step - loss: 1.0906 - acc: 0.3794 - auc_roc: 0.5223\n",
      "Epoch 2/10\n",
      "20457/20457 [==============================] - 3741s 183ms/step - loss: 1.0255 - acc: 0.4636 - auc_roc: 0.5731\n",
      "Epoch 3/10\n",
      "20457/20457 [==============================] - 3714s 182ms/step - loss: 0.9426 - acc: 0.5275 - auc_roc: 0.6256\n",
      "Epoch 4/10\n",
      "20457/20457 [==============================] - 3720s 182ms/step - loss: 0.9001 - acc: 0.5583 - auc_roc: 0.6619\n",
      "Epoch 5/10\n",
      "20457/20457 [==============================] - 3713s 181ms/step - loss: 0.8698 - acc: 0.5799 - auc_roc: 0.6870\n",
      "Epoch 6/10\n",
      "20457/20457 [==============================] - 3707s 181ms/step - loss: 0.8381 - acc: 0.6027 - auc_roc: 0.7060\n",
      "Epoch 7/10\n",
      "20457/20457 [==============================] - 3705s 181ms/step - loss: 0.8072 - acc: 0.6265 - auc_roc: 0.7218\n",
      "Epoch 8/10\n",
      "20457/20457 [==============================] - 3707s 181ms/step - loss: 0.7777 - acc: 0.6445 - auc_roc: 0.7354\n",
      "Epoch 9/10\n",
      "20457/20457 [==============================] - 3706s 181ms/step - loss: 0.7568 - acc: 0.6609 - auc_roc: 0.7472\n",
      "Epoch 10/10\n",
      "20457/20457 [==============================] - 3710s 181ms/step - loss: 0.7285 - acc: 0.6776 - auc_roc: 0.7579\n"
     ]
    }
   ],
   "source": [
    "rnn = build_model(3,\"rnn\")\n",
    "\n",
    "model_dict[\"rnn\"] = rnn.fit([docs_train,X_train],y_train,batch_size=32,epochs=10,verbose=1)\n",
    "\n",
    "rnn.save(\"Data/models/rnn.hdf5\")\n",
    "with open('Data/trainHistory/rnn.pkl', 'wb') as file_pi:\n",
    "    pickle.dump(model_dict[\"rnn\"].history, file_pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = build_model(3,\"cnn\")\n",
    "\n",
    "model_dict[\"cnn\"] = cnn.fit([docs_train,X_train],y_train,batch_size=64,epochs=10,verbose=1)\n",
    "\n",
    "cnn.save(\"Data/models/cnn.hdf5\")\n",
    "with open('Data/trainHistory/cnn.pkl', 'wb') as file_pi:\n",
    "    pickle.dump(model_dict[\"cnn\"].history, file_pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "20457/20457 [==============================] - 1743s 85ms/step - loss: 1.0774 - acc: 0.3837 - auc_roc: 0.5230\n",
      "Epoch 2/10\n",
      "20457/20457 [==============================] - 1745s 85ms/step - loss: 0.9801 - acc: 0.4933 - auc_roc: 0.6111\n",
      "Epoch 3/10\n",
      "20457/20457 [==============================] - 1747s 85ms/step - loss: 0.9208 - acc: 0.5341 - auc_roc: 0.6612\n",
      "Epoch 4/10\n",
      "20457/20457 [==============================] - 1747s 85ms/step - loss: 0.8800 - acc: 0.5669 - auc_roc: 0.6901\n",
      "Epoch 5/10\n",
      "20457/20457 [==============================] - 1745s 85ms/step - loss: 0.8368 - acc: 0.6042 - auc_roc: 0.7114\n",
      "Epoch 6/10\n",
      "20457/20457 [==============================] - 1750s 86ms/step - loss: 0.7917 - acc: 0.6345 - auc_roc: 0.7302\n",
      "Epoch 7/10\n",
      "20457/20457 [==============================] - 1753s 86ms/step - loss: 0.7507 - acc: 0.6623 - auc_roc: 0.7465\n",
      "Epoch 8/10\n",
      "20457/20457 [==============================] - 1752s 86ms/step - loss: 0.7111 - acc: 0.6897 - auc_roc: 0.7611\n",
      "Epoch 9/10\n",
      "20457/20457 [==============================] - 1747s 85ms/step - loss: 0.6720 - acc: 0.7106 - auc_roc: 0.7744\n",
      "Epoch 10/10\n",
      "20457/20457 [==============================] - 1752s 86ms/step - loss: 0.6447 - acc: 0.7229 - auc_roc: 0.7867\n"
     ]
    }
   ],
   "source": [
    "rnn_cnn = build_model(3,\"rnn_cnn\")\n",
    "\n",
    "model_dict[\"rnn_cnn\"] = rnn_cnn.fit([docs_train,X_train],y_train,batch_size=32,epochs=10,verbose=1)\n",
    "\n",
    "rnn_cnn.save(\"Data/models/rnn_cnn.hdf5\")\n",
    "with open('Data/trainHistory/rnn_cnn.pkl', 'wb') as file_pi:\n",
    "    pickle.dump(model_dict[\"rnn_cnn\"].history, file_pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model Evaluation\n",
    "#### Loss, Accuracy, and AUC_ROC graphs for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.read_pickle(\"Pickles/X_test.pkl\")\n",
    "y_test = pd.read_pickle(\"Pickles/y_test.pkl\")\n",
    "docs_test = np.load(\"Pickles/docs_test.npy\")\n",
    "\n",
    "mlp_hist = pickle.load(open(\"Data/trainHistory/mlp.pkl\",\"rb\"))\n",
    "cnn_hist = pickle.load(open(\"Data/trainHistory/cnn.pkl\",\"rb\"))\n",
    "rnn_hist = pickle.load(open(\"Data/trainHistory/rnn.pkl\",\"rb\"))\n",
    "rnn_cnn_hist = pickle.load(open(\"Data/trainHistory/rnn_cnn.pkl\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics(model_dict,metric,x_label,y_label):\n",
    "    plots = 1\n",
    "    plt.figure(figsize=[15,10])\n",
    "    for model, history in model_dict.items():\n",
    "        plt.subplot(2,2,plots)\n",
    "        plt.plot(history[metric])\n",
    "        #plt.plot(history.history['val_acc'])\n",
    "        plt.title('{0} {1}'.format(model,metric))\n",
    "        plt.ylabel(y_label)\n",
    "        plt.xlabel(x_label)\n",
    "        plots += 1\n",
    "    #plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"Graphs/{}.png\".format(metric),format=\"png\")\n",
    "    plt.show()\n",
    "plt.style.use(\"ggplot\")\n",
    "model_dict = {\"mlp\": mlp_hist,\n",
    "              \"cnn\": cnn_hist,\n",
    "              \"rnn\": rnn_hist,\n",
    "              \"rnn_cnn\": rnn_cnn_hist}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(model_dict,\"acc\",\"Epoch\",\"Accuracy\") ## Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(model_dict,\"loss\",\"Epoch\",\"Loss\") ## Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(model_dict,'auc_roc',\"Epoch\",\"AUC_ROC\") ## AUC_ROC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluation on Test Data\n",
    "The three metrics are listed as<br>\n",
    "[val_loss, val_accuracy, val_auc_roc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5013/5013 [==============================] - 8s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0787248851628497, 0.461998803117854, 0.5570867640658717]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.evaluate([docs_test,X_test],y_test,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5013/5013 [==============================] - 23s 5ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0734006599492474, 0.40394973070612455, 0.5751698227811426]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cnn.evaluate([docs_test,X_test],y_test,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5013/5013 [==============================] - 401s 80ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9135869847142052, 0.6295631359419185, 0.797258944419147]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn = load_model(\"Data/models/rnn.hdf5\",custom_objects={\"auc_roc\":auc_roc})\n",
    "rnn.evaluate([docs_test,X_test],y_test,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5013/5013 [==============================] - 194s 39ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9341351865366645, 0.6150009975137526, 0.7825800467697941]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_cnn = load_model(\"Data/models/rnn_cnn.hdf5\",custom_objects={\"auc_roc\":auc_roc})\n",
    "rnn_cnn.evaluate([docs_test,X_test],y_test,batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After 10 epochs, the RNN and RNN_CNN models generalize the best. We can try extending training for more epochs to see how training improves. I focus on the CNN-RNN model as it has had comparable accuracy to the RNN model and was trained in half the time.\n",
    "\n",
    "## 5. Extend Training for More Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20457 samples, validate on 5013 samples\n",
      "Epoch 1/20\n",
      "20457/20457 [==============================] - 3591s 176ms/step - loss: 0.6170 - acc: 0.7403 - auc_roc: 0.8936 - val_loss: 0.9954 - val_acc: 0.5783 - val_auc_roc: 0.8809\n",
      "Epoch 2/20\n",
      "20457/20457 [==============================] - 3583s 175ms/step - loss: 0.5874 - acc: 0.7540 - auc_roc: 0.8790 - val_loss: 1.0010 - val_acc: 0.6090 - val_auc_roc: 0.8800\n",
      "Epoch 3/20\n",
      "20457/20457 [==============================] - 3602s 176ms/step - loss: 0.5611 - acc: 0.7678 - auc_roc: 0.8814 - val_loss: 1.0650 - val_acc: 0.5466 - val_auc_roc: 0.8819\n",
      "Epoch 4/20\n",
      "20457/20457 [==============================] - 3675s 180ms/step - loss: 0.5452 - acc: 0.7749 - auc_roc: 0.8823 - val_loss: 0.9959 - val_acc: 0.6112 - val_auc_roc: 0.8838\n",
      "Epoch 5/20\n",
      "20457/20457 [==============================] - 3677s 180ms/step - loss: 0.5164 - acc: 0.7895 - auc_roc: 0.8856 - val_loss: 1.0316 - val_acc: 0.6346 - val_auc_roc: 0.8873\n",
      "Epoch 6/20\n",
      "20457/20457 [==============================] - 3635s 178ms/step - loss: 0.5006 - acc: 0.7958 - auc_roc: 0.8889 - val_loss: 1.0785 - val_acc: 0.6246 - val_auc_roc: 0.8902\n",
      "Epoch 7/20\n",
      "20457/20457 [==============================] - 3596s 176ms/step - loss: 0.4855 - acc: 0.8056 - auc_roc: 0.8916 - val_loss: 1.1877 - val_acc: 0.5468 - val_auc_roc: 0.8921\n",
      "Epoch 8/20\n",
      "20457/20457 [==============================] - 3593s 176ms/step - loss: 0.4763 - acc: 0.8081 - auc_roc: 0.8926 - val_loss: 1.0903 - val_acc: 0.6066 - val_auc_roc: 0.8937\n",
      "Epoch 9/20\n",
      "20457/20457 [==============================] - 3589s 175ms/step - loss: 0.4610 - acc: 0.8184 - auc_roc: 0.8948 - val_loss: 1.1224 - val_acc: 0.6044 - val_auc_roc: 0.8957\n",
      "Epoch 10/20\n",
      "20457/20457 [==============================] - 3591s 176ms/step - loss: 0.4490 - acc: 0.8188 - auc_roc: 0.8967 - val_loss: 1.0704 - val_acc: 0.6278 - val_auc_roc: 0.8978\n",
      "Epoch 11/20\n",
      "20457/20457 [==============================] - 3589s 175ms/step - loss: 0.4410 - acc: 0.8241 - auc_roc: 0.8988 - val_loss: 1.1902 - val_acc: 0.6082 - val_auc_roc: 0.8995\n",
      "Epoch 12/20\n",
      "20457/20457 [==============================] - 3589s 175ms/step - loss: 0.4278 - acc: 0.8297 - auc_roc: 0.9003 - val_loss: 1.1402 - val_acc: 0.5992 - val_auc_roc: 0.9011\n",
      "Epoch 13/20\n",
      "20457/20457 [==============================] - 3590s 176ms/step - loss: 0.4199 - acc: 0.8335 - auc_roc: 0.9019 - val_loss: 1.1853 - val_acc: 0.6447 - val_auc_roc: 0.9026\n",
      "Epoch 14/20\n",
      "20457/20457 [==============================] - 3588s 175ms/step - loss: 0.4081 - acc: 0.8420 - auc_roc: 0.9034 - val_loss: 1.1476 - val_acc: 0.5978 - val_auc_roc: 0.9041\n",
      "Epoch 15/20\n",
      "20457/20457 [==============================] - 3619s 177ms/step - loss: 0.3984 - acc: 0.8439 - auc_roc: 0.9049 - val_loss: 1.1500 - val_acc: 0.6150 - val_auc_roc: 0.9055\n",
      "Epoch 16/20\n",
      "20457/20457 [==============================] - 3680s 180ms/step - loss: 0.4012 - acc: 0.8430 - auc_roc: 0.9062 - val_loss: 1.1901 - val_acc: 0.6114 - val_auc_roc: 0.9067\n",
      "Epoch 17/20\n",
      "20457/20457 [==============================] - 3678s 180ms/step - loss: 0.3848 - acc: 0.8496 - auc_roc: 0.9074 - val_loss: 1.2949 - val_acc: 0.5697 - val_auc_roc: 0.9078\n",
      "Epoch 18/20\n",
      "20457/20457 [==============================] - 3698s 181ms/step - loss: 0.3740 - acc: 0.8538 - auc_roc: 0.9083 - val_loss: 1.1910 - val_acc: 0.6260 - val_auc_roc: 0.9089\n",
      "Epoch 19/20\n",
      "20457/20457 [==============================] - 3700s 181ms/step - loss: 0.3693 - acc: 0.8579 - auc_roc: 0.9096 - val_loss: 1.1846 - val_acc: 0.6174 - val_auc_roc: 0.9101\n",
      "Epoch 20/20\n",
      "20457/20457 [==============================] - 3650s 178ms/step - loss: 0.3680 - acc: 0.8562 - auc_roc: 0.9106 - val_loss: 1.3391 - val_acc: 0.6383 - val_auc_roc: 0.9110\n"
     ]
    }
   ],
   "source": [
    "history = rnn_cnn.fit(x = [docs_train,X_train],\n",
    "                      y = y_train,\n",
    "                      batch_size = 32,\n",
    "                      epochs = 20,\n",
    "                      verbose = 1,\n",
    "                      validation_data = ([docs_test,X_test],y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnn_cnn.save(\"Data/models/rnn_cnn1.hdf5\")\n",
    "with open('Data/trainHistory/rnn_cnn1.pkl', 'wb') as file_pi:\n",
    "    pickle.dump(history.history, file_pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Validation accuracy doesn't seem to be improving much, we can try training for another few epochs to be safe..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20457 samples, validate on 5013 samples\n",
      "Epoch 1/5\n",
      "20457/20457 [==============================] - 3605s 176ms/step - loss: 0.3564 - acc: 0.8617 - auc_roc: 0.9115 - val_loss: 1.1948 - val_acc: 0.6010 - val_auc_roc: 0.9120\n",
      "Epoch 2/5\n",
      "20457/20457 [==============================] - 3605s 176ms/step - loss: 0.3480 - acc: 0.8661 - auc_roc: 0.9125 - val_loss: 1.2644 - val_acc: 0.5891 - val_auc_roc: 0.9130\n",
      "Epoch 3/5\n",
      "20457/20457 [==============================] - 3603s 176ms/step - loss: 0.3496 - acc: 0.8641 - auc_roc: 0.9134 - val_loss: 1.2522 - val_acc: 0.6369 - val_auc_roc: 0.9139\n",
      "Epoch 4/5\n",
      "20457/20457 [==============================] - 3589s 175ms/step - loss: 0.3410 - acc: 0.8680 - auc_roc: 0.9144 - val_loss: 1.4392 - val_acc: 0.6351 - val_auc_roc: 0.9147\n",
      "Epoch 5/5\n",
      "20457/20457 [==============================] - 3592s 176ms/step - loss: 0.3411 - acc: 0.8712 - auc_roc: 0.9150 - val_loss: 1.2874 - val_acc: 0.6022 - val_auc_roc: 0.9154\n"
     ]
    }
   ],
   "source": [
    "history2 = rnn_cnn.fit(x = [docs_train,X_train],\n",
    "                      y = y_train,\n",
    "                      batch_size = 32,\n",
    "                      epochs = 5,\n",
    "                      verbose = 1,\n",
    "                      validation_data = ([docs_test,X_test],y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It doesn't look more epochs will improve the model any further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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